rossmann

Rossmann Retail Sales Prediction

End-to-end sales analytics on 844K+ Rossmann retail records across 1,115 German stores, using PostgreSQL, Python, and Power BI.

Machine Learning project for forecasting future sales using Rossmann Store Sales data, feature engineering, Random Forest, XGBoost, and business insights.

Time series sales forecasting — Prophet vs LightGBM on Rossmann dataset. MAPE 9.47%. Deployed on Streamlit Cloud.

End-to-end ML pipeline forecasting daily retail sales on the Rossmann Store Sales dataset (1M records, 1,115 stores). Compares Linear Regression, Random Forest, and XGBoost with time-series cross-validation. Tuned XGBoost achieves R² = 0.89 on a 6-week-ahead test window.

Rossmann product price extractor

DEPI team project predicting Rossmann store sales — EDA, feature engineering, and an ML pipeline tuned with GridSearchCV (final XGBoost model: 97.9% R2). Deployed via Streamlit, FastAPI, and Power BI.

Rossmann store sales forecasting: Prophet vs SARIMA vs LightGBM. MAPE 8.20% (LightGBM winner, 2x better than classical models). Walk-forward CV + anomaly detection on 1M rows.

Time series sales forecast for Rossmann Pharmaceuticals

🏪 The project's idea is to use a machine learning model to predict the sales quantity that each store will have in the next six weeks, assisting managers in their future decision-making.